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<body>
<pre><code class="r">##############################################################
##############################################################
### Replication Materials for
### Stefan Müller and Liam Kneafsey:
### Evidence for the Irrelevance of Irrelevant Events
### Political Science Research and Methods
###
### Please get in touch with the authors if you have any questions: 
### stefan.mueller@ucd.ie
### Note: 0000_readme.pdf contains 
### instructions and details about each script and dataset
##############################################################
##############################################################


## load packages
library(dplyr)   # CRAN v1.0.5
library(tidyr)   # CRAN v1.1.3
library(MuMIn)   # CRAN v1.43.17
library(cowplot) # CRAN v1.1.1
library(car)     # CRAN v3.0-10
library(ggplot2) # CRAN v3.3.3
library(stringr) # CRAN v1.4.0

source(&quot;function_theme_base.R&quot;)


here::here()
</code></pre>

<pre><code>## [1] &quot;/Users/smueller/Dropbox/papers/ireland_incumbency_gaa&quot;
</code></pre>

<pre><code class="r">## alternatively, set working directory manually
## setwd(&quot;...&quot;)


## parts of this code are adjusted from
## https://dcosme.github.io/2019/02/26/specification-curve-example/

## load dataset with elections
dat_general_raw &lt;- readRDS(&quot;data_elections_general_full.rds&quot;) 


## only keep: politicians from incumbent government parties
## whose county either lost or won a game
## the coefficient for winner_loser_before_num_num will show 
## whether an incumbent increases her vote share when the county team
## won shortly before the election (winner_loser_before_num = 1)

dat_general_incumbents_treated &lt;- dat_general_raw %&gt;% 
    filter(incumbency_status_party == &quot;Party: Government&quot;) %&gt;% 
    filter(winner_loser_before != &quot;Untreated&quot;) %&gt;% 
    mutate(winner_loser_before = as.character(winner_loser_before)) %&gt;% 
    mutate(winner_loser_before_num = ifelse(winner_loser_before == &quot;Win&quot;, 1, 0))

table(dat_general_incumbents_treated$incumbency_status_party)
</code></pre>

<pre><code>## 
## Party: Government 
##               371
</code></pre>

<pre><code class="r">table(dat_general_incumbents_treated$winner_loser_before)
</code></pre>

<pre><code>## 
## Defeat    Win 
##    161    210
</code></pre>

<pre><code class="r">#                # create a dummy for strongholds
dat_general_incumbents_treated &lt;- dat_general_incumbents_treated %&gt;% 
    mutate(stronghold_dummy = ifelse(str_detect(stronghold_win_loss, &quot;Stronghold&quot;), 
                                     &quot;Stronghold&quot;, &quot;Non-stronghold&quot;))

## set na.action for dredge
options(na.action = &quot;na.fail&quot;)


## select only the variables included in the regression models 
## and omit missing values to have the same N across all models

dat_general_incumbents_treated_no_na &lt;- dat_general_incumbents_treated %&gt;% 
    ungroup() %&gt;% 
    select(diff_share, county_gaa, 
           winner_loser_before_num, turnout, 
           election_id, sport, margin,
           party_recoded, stronghold_dummy) %&gt;% 
    na.omit() 


summary(dat_general_incumbents_treated_no_na$winner_loser_before_num)
</code></pre>

<pre><code>##    Min. 1st Qu.  Median 
##  0.0000  0.0000  1.0000 
##    Mean 3rd Qu.    Max. 
##  0.6389  1.0000  1.0000
</code></pre>

<pre><code class="r">## number of complete observations in this subset
nrow(dat_general_incumbents_treated_no_na)
</code></pre>

<pre><code>## [1] 144
</code></pre>

<pre><code class="r">## run full model
full_model_general &lt;- lm(diff_share ~ 
                             winner_loser_before_num +
                             county_gaa +
                             turnout +
                             margin +
                             election_id +
                             sport +
                             party_recoded +
                             stronghold_dummy,
                         data = dat_general_incumbents_treated_no_na)


## run all possible nested models but only keep thos models that include
## the treatment variable (winner_loser_before)

all_models_general &lt;-  dredge(full_model_general) 
</code></pre>

<pre><code>## Fixed term is &quot;(Intercept)&quot;
</code></pre>

<pre><code class="r">nrow(all_models_general)
</code></pre>

<pre><code>## [1] 256
</code></pre>

<pre><code class="r">## filter only models that include the main independent variable of interest (winner/loser)
all_models_general &lt;- filter(all_models_general, !is.na(winner_loser_before_num))

nrow(all_models_general)
</code></pre>

<pre><code>## [1] 128
</code></pre>

<pre><code class="r">## get coefficients and standard errors for all estimates in each model
all_models_general_coefs &lt;- coefTable(all_models_general)

length(all_models_general_coefs)
</code></pre>

<pre><code>## [1] 256
</code></pre>

<pre><code class="r">## transform list of coefficients and standard errors to a data frame
df_all_models_general_coefs &lt;- do.call(rbind.data.frame, all_models_general_coefs)

## get coefficient name from rownames
df_all_models_general_coefs$coefficient &lt;- rownames(df_all_models_general_coefs)


## filter only those coefficients relating to winner_loser_before_num
## because these values are needed to extract the standard errors of the coefficients
df_coefs_se_winner_loser_before &lt;- filter(df_all_models_general_coefs, 
                                          str_detect(coefficient, &quot;winner_loser&quot;)) 

#                # create variable for merging below
df_coefs_se_winner_loser_before &lt;- df_coefs_se_winner_loser_before %&gt;% 
    mutate(winner_loser_before_num = Estimate) 

## merge standard errors for the independent variable of interest
## by using the point estimates of winner_loser_before_num
## as the merging variable (not elegant, but not possible to do it differently)

## check that estimate colums are unique 
unique_valid_models &lt;- nrow(filter(all_models_general, !is.na(winner_loser_before_num)))
unique_valid_models
</code></pre>

<pre><code>## [1] 128
</code></pre>

<pre><code class="r">unique_coefs &lt;- length(unique(all_models_general$winner_loser_before_num))
unique_coefs
</code></pre>

<pre><code>## [1] 128
</code></pre>

<pre><code class="r">unique_coefs_se &lt;- length(unique(df_coefs_se_winner_loser_before$winner_loser_before_num))
unique_coefs_se
</code></pre>

<pre><code>## [1] 128
</code></pre>

<pre><code class="r">## this code works only if the number of unique 
## coefficients is the same in both dataframes!
## Be very careful if you adjust this code for your own work.

## stop the code if the numbers differ
stopifnot(unique_coefs_se == unique_coefs)


all_models_winner_loser_before &lt;- left_join(all_models_general,
                                            df_coefs_se_winner_loser_before,
                                            by = c(&quot;winner_loser_before_num&quot;))


nrow(all_models_winner_loser_before)
</code></pre>

<pre><code>## [1] 128
</code></pre>

<pre><code class="r">nrow(all_models_general)
</code></pre>

<pre><code>## [1] 128
</code></pre>

<pre><code class="r">## get variables included in the model
variable.names &lt;- names(dat_general_incumbents_treated_no_na)[-1]


## prepare data for plot
plot_data_incumbentparty &lt;-  all_models_winner_loser_before %&gt;%
    filter(!is.na(Estimate)) %&gt;% ## remove models without coefficient for winner_loser_before_num
    arrange(Estimate) %&gt;% ## arrange estimates in ascending order
    mutate(specification = row_number()) 


## check how many coefficients are positive/negative and how many are
## statistically significant

plot_data_incumbentparty_check &lt;- plot_data_incumbentparty %&gt;% 
    mutate(significant_95 = (Estimate - 1.96 * `Std. Error`) &gt; 0 |
               (Estimate + 1.96 * `Std. Error`) &lt; 0) %&gt;% 
    mutate(direction = ifelse(Estimate &gt; 0, &quot;Positive&quot;,
                              ifelse(Estimate &lt; 0, &quot;Negative&quot;, &quot;0&quot;)))


table(plot_data_incumbentparty_check$direction,
      plot_data_incumbentparty_check$significant_95)
</code></pre>

<pre><code>##           
##            FALSE TRUE
##   Negative    79    0
##   Positive    42    7
</code></pre>

<pre><code class="r">nrow(plot_data_incumbentparty) ## number of models
</code></pre>

<pre><code>## [1] 128
</code></pre>

<pre><code class="r">plot_top_incumbentparty &lt;- plot_data_incumbentparty %&gt;%
    ggplot(aes(x = specification, 
               y = Estimate)) + 
    geom_linerange(aes(ymin = Estimate - 1.96 * `Std. Error`,
                       ymax = Estimate + 1.96 * `Std. Error`),
                   colour = &quot;grey50&quot;) +
    geom_point(size = 1) +
    geom_hline(yintercept = 0, linetype = &quot;dashed&quot;, color = &quot;red&quot;) + ## dashed line for null model AIC
    labs(x = &quot;&quot;, y = &quot;Coefficient estimate for Winner&quot;)
plot_top_incumbentparty
</code></pre>

<p><img src="data:image/png;base64,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" alt="plot of chunk unnamed-chunk-1"/></p>

<pre><code class="r">## note: warning will appear because not all models contain all variables 
## (therefore, the attributes are not identical and will be dropped)

plot_data_incumbentparty_long &lt;- plot_data_incumbentparty %&gt;%
    gather(variable, value, eval(variable.names)) %&gt;% 
    mutate(value = ifelse(!is.na(value), &quot;|&quot;, &quot;&quot;)) ## plot a line if included in the model
</code></pre>

<pre><code>## Warning: attributes are not identical across measure variables;
## they will be dropped
</code></pre>

<pre><code class="r">## remove winner_loser_before_num because this variable is 
## included in all models and this coefficient is displayed in the graph

plot_data_incumbentparty_long &lt;- plot_data_incumbentparty_long %&gt;% 
    filter(variable != &quot;winner_loser_before_num&quot;)


table(plot_data_incumbentparty_long$variable)
</code></pre>

<pre><code>## 
##       county_gaa 
##              128 
##      election_id 
##              128 
##           margin 
##              128 
##    party_recoded 
##              128 
##            sport 
##              128 
## stronghold_dummy 
##              128 
##          turnout 
##              128
</code></pre>

<pre><code class="r">recode_vars &lt;- c(&quot;&#39;county_gaa&#39;=&#39;County team&#39;;
                 &#39;difference&#39;=&#39;Days after match&#39;;
                 &#39;election_id&#39;=&#39;Election&#39;;
                 &#39;margin&#39;=&#39;Margin of result&#39;;
                 &#39;party_recoded&#39;=&#39;Party&#39;;
                 &#39;sport&#39;=&#39;Sport&#39;;
                 &#39;stronghold_dummy&#39;=&#39;Stronghold&#39;;
                 &#39;turnout&#39;=&#39;Turnout&#39;&quot;)



plot_data_incumbentparty_long &lt;- plot_data_incumbentparty_long %&gt;% 
    mutate(variable = car::recode(variable, recode_vars))


plot_bottom_incumbentparty &lt;- ggplot(plot_data_incumbentparty_long,
                                     aes(specification, variable)) +
    geom_text(aes(label = value), alpha = 1) +
    labs(x = &quot;Specification number&quot;, y = &quot;Variables&quot;)

plot_bottom_incumbentparty
</code></pre>

<p><img 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" alt="plot of chunk unnamed-chunk-1"/></p>

<pre><code class="r">## combine both plots
plot_grid(plot_top_incumbentparty, plot_bottom_incumbentparty, ncol = 1, align = &quot;v&quot;,
          rel_heights = c(0.6, 0.4))
</code></pre>

<p><img 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" alt="plot of chunk unnamed-chunk-1"/></p>

<pre><code class="r">ggsave(&quot;fig_a17.pdf&quot;,
       width = 9, height = 5.5)


## script executed successfully on
date()
</code></pre>

<pre><code>## [1] &quot;Wed Jun 23 09:57:48 2021&quot;
</code></pre>

<pre><code class="r">sessionInfo()
</code></pre>

<pre><code>## R version 4.0.2 (2020-06-22)
## Platform: x86_64-apple-darwin17.0 (64-bit)
## Running under: macOS  10.16
## 
## Matrix products: default
## LAPACK: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRlapack.dylib
## 
## locale:
## [1] en_IE.UTF-8/en_IE.UTF-8/en_IE.UTF-8/C/en_IE.UTF-8/en_IE.UTF-8
## 
## attached base packages:
## [1] grid      stats    
## [3] graphics  grDevices
## [5] utils     datasets 
## [7] methods   base     
## 
## other attached packages:
##  [1] forcats_0.5.1     
##  [2] haven_2.3.1       
##  [3] scales_1.1.1      
##  [4] Hmisc_4.4-2       
##  [5] Formula_1.2-4     
##  [6] lattice_0.20-41   
##  [7] parameters_0.13.0 
##  [8] MuMIn_1.43.17     
##  [9] ebal_0.1-6        
## [10] jtools_2.1.2      
## [11] lubridate_1.7.10  
## [12] broom.mixed_0.2.6 
## [13] broom_0.7.4       
## [14] lme4_1.1-26       
## [15] survey_4.0        
## [16] survival_3.2-7    
## [17] Matrix_1.3-2      
## [18] WeightIt_0.11.0   
## [19] cobalt_4.2.4      
## [20] car_3.0-10        
## [21] carData_3.0-4     
## [22] cowplot_1.1.1     
## [23] here_1.0.1        
## [24] modelsummary_0.8.0
## [25] ggeffects_1.0.1   
## [26] estimatr_0.30.2   
## [27] ggplot2_3.3.3     
## [28] stringr_1.4.0     
## [29] tidyr_1.1.3       
## [30] dplyr_1.0.5       
## [31] knitr_1.31        
## 
## loaded via a namespace (and not attached):
##   [1] readxl_1.3.1       
##   [2] uuid_0.1-4         
##   [3] backports_1.2.1    
##   [4] systemfonts_1.0.1  
##   [5] plyr_1.8.6         
##   [6] TMB_1.7.19         
##   [7] splines_4.0.2      
##   [8] TH.data_1.0-10     
##   [9] digest_0.6.27      
##  [10] htmltools_0.5.1.1  
##  [11] fansi_0.4.2        
##  [12] magrittr_2.0.1     
##  [13] checkmate_2.0.0    
##  [14] cluster_2.1.0      
##  [15] openxlsx_4.2.3     
##  [16] officer_0.3.16     
##  [17] sandwich_3.0-0     
##  [18] jpeg_0.1-8.1       
##  [19] colorspace_2.0-0   
##  [20] rvest_0.3.6        
##  [21] ggrepel_0.9.1      
##  [22] mitools_2.4        
##  [23] xfun_0.20          
##  [24] crayon_1.4.1       
##  [25] zoo_1.8-8          
##  [26] glue_1.4.2         
##  [27] kableExtra_1.3.1   
##  [28] gtable_0.3.0       
##  [29] emmeans_1.5.4      
##  [30] webshot_0.5.2      
##  [31] abind_1.4-5        
##  [32] annotater_0.1.3    
##  [33] mvtnorm_1.1-1      
##  [34] DBI_1.1.1          
##  [35] Rcpp_1.0.6         
##  [36] viridisLite_0.4.0  
##  [37] xtable_1.8-4       
##  [38] htmlTable_2.1.0    
##  [39] foreign_0.8-81     
##  [40] stats4_4.0.2       
##  [41] htmlwidgets_1.5.3  
##  [42] httr_1.4.2         
##  [43] RColorBrewer_1.1-2 
##  [44] ellipsis_0.3.2     
##  [45] pkgconfig_2.0.3    
##  [46] farver_2.1.0       
##  [47] nnet_7.3-15        
##  [48] utf8_1.2.1         
##  [49] tidyselect_1.1.1   
##  [50] labeling_0.4.2     
##  [51] rlang_0.4.11       
##  [52] reshape2_1.4.4     
##  [53] munsell_0.5.0      
##  [54] cellranger_1.1.0   
##  [55] tools_4.0.2        
##  [56] cli_2.5.0          
##  [57] generics_0.1.0     
##  [58] sjlabelled_1.1.7   
##  [59] evaluate_0.14      
##  [60] tables_0.9.6       
##  [61] zip_2.1.1          
##  [62] pander_0.6.3       
##  [63] purrr_0.3.4        
##  [64] nlme_3.1-152       
##  [65] mime_0.10          
##  [66] xml2_1.3.2         
##  [67] compiler_4.0.2     
##  [68] rstudioapi_0.13    
##  [69] curl_4.3           
##  [70] png_0.1-7          
##  [71] tibble_3.1.1       
##  [72] statmod_1.4.35     
##  [73] stringi_1.5.3      
##  [74] highr_0.8          
##  [75] gdtools_0.2.3      
##  [76] texreg_1.37.5      
##  [77] nloptr_1.2.2.2     
##  [78] markdown_1.1       
##  [79] vctrs_0.3.8        
##  [80] pillar_1.6.0       
##  [81] lifecycle_1.0.0    
##  [82] estimability_1.3   
##  [83] data.table_1.14.0  
##  [84] insight_0.14.0     
##  [85] flextable_0.6.3    
##  [86] R6_2.5.0           
##  [87] latticeExtra_0.6-29
##  [88] gridExtra_2.3      
##  [89] rio_0.5.16         
##  [90] codetools_0.2-18   
##  [91] boot_1.3-27        
##  [92] MASS_7.3-53        
##  [93] assertthat_0.2.1   
##  [94] rprojroot_2.0.2    
##  [95] withr_2.4.2        
##  [96] multcomp_1.4-16    
##  [97] mgcv_1.8-33        
##  [98] bayestestR_0.8.2   
##  [99] hms_1.0.0          
## [100] rpart_4.1-15       
## [101] coda_0.19-4        
## [102] minqa_1.2.4        
## [103] rmarkdown_2.6      
## [104] snakecase_0.11.0   
## [105] base64enc_0.1-3
</code></pre>

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